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The halloysite nanotubes (HNTs) are naturally found in New Zealand, China, Brazil, America, and France. Halloysite nanotubes have unique physical, chemical, and electrochemical properties. Due to their non-hazardous nature and easy availability, they are used in various fields of science. To improve halloysite nanotubes' interactions, these can be functionalized by different modification methods like covalent functionalization, non-covalent functionalization, etc. The chemical formula of halloysite nanotubes is Al 2 Si 2 O 5 (OH) 4 .2H 2 O. HNTs have a nanotubular geometry which exhibit its dimensions on nanoscale. This nanotubular array of HNTs varies with different regions. In this review, we have tried to reiterate the key properties and various methods to modify halloysite nanotubes.

G protein-coupled receptors (GPCRs) are transmembrane proteins, which transduce signals from extracellular ligands to intracellular G protein. Automatic classification of GPCRs can provide important information for the development of novel drugs in pharmaceutical industry. In this paper, we propose an evolutionary approach, GPCR-MPredictor, which combines individual classifiers for predicting GPCRs. GPCR-MPredictor is a web predictor that can efficiently predict GPCRs at five levels. The first level determines whether a protein sequence is a GPCR or a non-GPCR. If the predicted sequence is a GPCR, then it is further classified into family, subfamily, sub-subfamily, and subtype levels. In this work, our aim is to analyze the discriminative power of different feature extraction and classification strategies in case of GPCRs prediction and then to use an evolutionary ensemble approach for enhanced prediction performance. Features are extracted using amino acid composition, pseudo amino acid composition, and dipeptide composition of protein sequences. Different classification approaches, such as k-nearest neighbor (KNN), support vector machine (SVM), probabilistic neural networks (PNN), J48, Adaboost, and Naives Bayes, have been used to classify GPCRs. The proposed hierarchical GA-based ensemble classifier exploits the prediction results of SVM, KNN, PNN, and J48 at each level. The GA-based ensemble yields an accuracy of 99.75, 92.45, 87.80, 83.57, and 96.17% at the five levels, on the first dataset. We further perform predictions on a dataset consisting of 8,000 GPCRs at the family, subfamily, and sub-subfamily level, and on two other datasets of 365 and 167 GPCRs at the second and fourth levels, respectively. In comparison with the existing methods, the results demonstrate the effectiveness of our proposed GPCR-MPredictor in classifying GPCRs families. It is accessible at http://111.68.99.218/gpcr-mpredictor/.

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Despite immense potential applications envisioned for self-cleaning surfaces, the existing approaches used for fabricating these surfaces suffer limitations arising from their high cost and complicated fabrication procedures, as well as the lack of mechanical durability and poor optical clarity. Herein we report a simple fabrication design toward omniphobic surfaces involving the application of an epoxy thermoset onto a substrate and the subsequent addition of a self-cleaning component. This approach is environmentally benign, as it utilizes a nonfluorinated self-cleaning component, namely polydimethylsiloxane (PDMS). This method is highly versatile, as was demonstrated by its successful use in an epoxy and its composites with a nanoclay, cellulose nanocrystal, and graphene oxide. The self-cleaning-, optical-, and mechanical properties of these coatings are systematically evaluated herein. In addition, scanning electron microscopy (SEM) characterization is performed to gain valuable insight regarding the morphology of PDMS in these epoxy coatings.